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pandas: powerful Python data analysis toolkit

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What is it?

pandas is a Python package providing fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, real world data analysis in Python. Additionally, it has
the broader goal of becoming the most powerful and flexible open source data
analysis / manipulation tool available in any language. It is already well on
its way towards this goal.

Main Features

Here are just a few of the things that pandas does well:

Easy handling of missing data (represented as
NaN) in floating point as well as non-floating point data

Automatic and explicit data alignment: objects can
be explicitly aligned to a set of labels, or the user can simply
ignore the labels and let Series, DataFrame, etc. automatically
align the data for you in computations

Powerful, flexible group by functionality to perform
split-apply-combine operations on data sets, for both aggregating
and transforming data

Make it easy to convert ragged,
differently-indexed data in other Python and NumPy data structures
into DataFrame objects

Getting Help

Discussion and Development

Most development discussion is taking place on github in this repo. Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Gitter channel is available for quick development related questions.

If you are simply looking to start working with the pandas codebase, navigate to the GitHub "issues" tab and start looking through interesting issues. There are a number of issues listed under Docs and good first issue where you could start out.

You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to subscribe to pandas on CodeTriage.

Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!